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Record W4400482686 · doi:10.55016/ojs/cpai.v1i2.56983

Academic Integrity Pledges -- Acculturating Students to Integrity within Canadian Higher Education

2018· article· en· W4400482686 on OpenAlexaffabout
Jennie Miron, Krisstine Fenning

Bibliographic record

VenueCanadian Perspectives on Academic Integrity · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsHumber Polytechnic
Fundersnot available
KeywordsAcademic integrityStructural integrityTerritorial integrityResearch integrityPolitical sciencePersonal IntegrityMembrane integrityPsychologyLawEngineeringPublic relationsSocial psychologyChemistry

Abstract

fetched live from OpenAlex

Academic integrity, and its associated values, are an essential aspect of cultures that provide a foundation for conduct and behaviour of all members within higher education. Establishing such cultures support the quality of student learning as well as their ethical deportment within their programs of study. The merits of acculturating students within academic integrity has implications for their performance and commitment to similar conduct and behaviours in their chosen professional fields as positive, ethical, and caring professionals benefitting all who receive their care and service. Creating and sustaining such learning cultures requires a multifaceted approach and an understanding and appreciation for the complexity of nurturing such environments. Academic integrity pledges situated across the learning trajectory at meaningful times during students' developmental paths, serve as one strategy that can be effective to academic integrity efforts. How one School of Health Sciences has approached and realized academic integrity pledges are discussed, and may serve as an example for others. The successes and opportunities for future development are outlined and reviewed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0040.000
Research integrity0.0060.035
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.386
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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